Vehicle surroundings monitoring apparatus

ABSTRACT

A vehicle surroundings monitoring apparatus that extracts a body present in the surroundings of a vehicle as an object, based on an image captured by an infrared imaging device, including a binarized object extraction device that extracts a binarized object from image data obtained by binarizing a gray scale image of the image; a horizontal edge detection device that detects a horizontal edge in the gray scale image; a horizontal edge determination device that determines whether or not the horizontal edge detected by the horizontal edge detection device exists in a prescribed range from the top end or bottom end of the binarized object extracted by the binarized object extraction device; and an object type determination device that determines a type of object based on a determination result of the horizontal edge determination device.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a vehicle surroundings monitoring apparatus that extracts objects by performing binarization processing of images taken by infrared cameras.

Priority is claimed on Japanese Patent Application Publication No. 2004-347817, filed Nov. 30, 2004, the content of which is incorporated herein by reference.

2. Description of Related Art

Conventionally, a display processing device is known in which an object such as a pedestrian with a possibility of colliding with a vehicle is extracted from an infrared image of a vehicles surroundings captured by an infrared camera, and information of this object is provided to the driver (for example, see Japanese Unexamined Patent Application, First Publication No. H11-328364).

This display processing device searches a region (binarized object) where bright parts are concentrated by binarizing the infrared image, and determines whether or not the binarized object is a pedestrian's head based on the distance calculated from an aspect ratio or filling factor of the binarized object, and also an actual area and a center of gravity on the infrared image. Then, the height of the pedestrian on the infrared image is calculated from the distance between the head part region of the pedestrian and the infrared camera, and an average height of adult persons, and a body region containing the body of a pedestrian is set. By separating and displaying the head region and body region from other regions, visual aid for the driver is performed with respect to a pedestrian.

Incidentally, since the display processing device of the example of the related art mentioned above detects a pedestrian based on determination of the shape for a head part region or body part region on the infrared image, it may become difficult to distinguish a pedestrian from a manmade structure that has a shape of a pedestrian, and particularly a similar shape, size, and position height of the pedestrian's head and that emits heat.

SUMMARY OF THE INVENTION

The present invention takes into consideration the abovementioned circumstances, with an object of providing a vehicle surroundings monitoring apparatus that is capable of precisely distinguishing and extracting a pedestrian and a manmade structure on an infrared image.

In order to solve the above problem and achieve the related object, the present invention provides a vehicle surroundings monitoring apparatus that extracts a body present in the surroundings of a vehicle as an object, based on an image captured by an infrared imaging device, including a binarized object extraction device that extracts a binarized object from image data obtained by binarizing a gray scale images of the image; a horizontal edge detection device that detects a horizontal edge in the gray scale image; a horizontal edge determination device that determines whether or not the horizontal edge detected by the horizontal edge detection device exists in a prescribed range from top end or bottom end of the binarized object extracted by the binarized object extraction device; and an object type determination device that determines a type of object based on a determination result of the horizontal edge determination device.

According to the vehicle surroundings monitoring apparatus described above, a manmade structure as a type of object in which horizontal edges are difficult to detect in a prescribed range from top end or bottom end of the binarized object and something other than a manmade structure, for example a pedestrian having horizontal edges easily detected at the top end or the bottom end of the binarized object, can be precisely distinguished.

The vehicle surroundings monitoring apparatus may further include a pedestrian recognition device that recognizes pedestrians present in the surroundings of a vehicle based on the image, the pedestrian recognition device executing pedestrian recognition processing on the object when the object is determined to be something other than a manmade structure, or to be a pedestrian, by the object type determination device.

In this case, pedestrian recognition accuracy can be improved by performing the pedestrian recognition processing for the object determined to be something other than a manmade structure as well as for the object determined to be a pedestrian.

Furthermore, the vehicle surroundings monitoring apparatus may further include a warning output device that outputs a warning directed to the object when the object is determined by the object type determination device to be something other than a manmade structure or to be a pedestrian.

In this case, since a warning can be output for an object determined to be something other than a manmade structure as well as an object determined to be a pedestrian, unnecessary warnings for a manmade structure can be avoided.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing the construction of a vehicle surroundings monitoring apparatus according to an embodiment of the present invention.

FIG. 2 is a diagram showing a vehicle equipped with the vehicle surroundings monitoring apparatus shown in FIG. 1.

FIG. 3 is a flowchart showing the operations of the vehicle surroundings monitoring apparatus shown in FIG. 1.

FIG. 4 is a flowchart showing the warning determination processing shown in FIG. 3.

FIG. 5 is a diagram showing an example of a relative position between a vehicle and an object.

FIG. 6 is a diagram showing an example of the classification of the regions such as an approach determination region set in front of the vehicle.

FIG. 7 is a diagram showing an example of a target region (mask) that is a horizontal edge detection target, being a region that includes a binarized object.

FIG. 8 is a diagram showing an example of a horizontal edge detected in the mask shown in FIG. 7.

FIG. 9 is a diagram showing an example of the edge detection position where the height position of the horizontal edge is a maximum (that is, the highest position in the vertical direction).

FIG. 10 is a diagram showing an example of the edge detection position and the top end position of the binarized object.

FIG. 11 is a diagram showing an example of a target region (mask) that is a horizontal edge detection target, being a region that includes at least the lower portion of the binarized object.

FIG. 12 is a diagram showing an example of the horizontal edge detected in the mask shown in FIG. 11.

FIG. 13 is a diagram showing an example of the edge detection position and the bottom end position of the binarized object.

DETAILED DESCRIPTION OF THE INVENTION

Hereunder, a vehicle surroundings monitoring apparatus according to one embodiment of the present invention is described with reference to the drawings.

The vehicle surroundings monitoring apparatus according to the present embodiment, for example as shown in FIG. 1, includes: an image processing unit 1 equipped with a CPU (Central Processing Unit) that controls the vehicle surroundings monitoring apparatus; two infrared cameras 2R and 2L that are capable of detecting distant infrared radiation; a yaw rate sensor 3 that detects the yaw rate of the vehicle; a vehicle speed sensor 4 that detects the traveling speed of the vehicle; a brake sensor 5 that detects a driver's braking operation; a speaker 6; and a display device 7. For example, the image processing unit 1 detects a moving object such as a pedestrian or an animal in front of the vehicle in its traveling direction from infrared images of the surroundings of the vehicle that are captured by the two infrared cameras 2R and 2L, and from detection signals relating to the traveling status of the vehicle that are detected by each of the sensors 3, 4, and 5. In the case where the possibility of a collision between the detected moving object and the vehicle is determined, a warning is output via the speaker 6 or the display device 7.

Moreover, the display device 7 is, for example, constructed including a display device integrated with gauges that display various traveling states of the vehicle, a display device such as a navigation device, and furthermore an HUD (Head Up Display) 7 a that displays various information at a position on the front window where the field of front vision of the driver is not impaired.

In addition, the image processing unit 1 includes an A/D converter, that converts input analog signals to digital signals, an image memory, that stores digitized image signals, a CPU (central processing unit), that performs various arithmetic processing, a RAM (Random Access Memory), that is used for storing data in the middle of the arithmetic processing, a ROM (Read Only Memory), that stores programs that are performed by the CPU, and tables, maps and the like, and an output circuit that outputs drive signals for the speaker 6 and display signals for the HUD 7 a. The image-processing unit 1 is constructed such that the output signals of the infrared cameras 2R and 2L, and the respective sensors, 3, 4, and 5 are input into the CPU after being converted to digital signals.

Furthermore, as shown in FIG. 2, two infrared cameras 2R and 2L are disposed at the front of the vehicle 10 at positions symmetrical in the width direction relative to the central axis of the vehicle 10. The optical axes of both cameras 2R and 2L are parallel to each other, and both infrared cameras 2R and 2L are secured at the same height from the road surface. A characteristic of the infrared cameras 2R and 2L is that the output signal level (that is, luminance) increases as the temperature of the object increases.

Moreover, the HUD 7 a is provided so as to display the images at a position on the front window of the vehicle 10, where the field of front vision of the driver is not impaired.

The vehicle surroundings monitoring apparatus according to the present embodiment is provided with the construction described above. Next, the operation of the vehicle surroundings monitoring apparatus is described, with reference to the drawings.

The operations in the image processing unit 1 for the detection of an object such as a pedestrian, and outputting a warning, are described below.

First of all, in step S1 shown in FIG. 3, the image processing unit 1 obtains infrared images, which are the output signals from the infrared cameras 2R and 2L.

Next, in step S2, A/D conversion of the obtained images is performed.

Next, in step S3, a gray scale image containing half tone gradation information is obtained, and stored in an image memory. Here the infrared camera 2R acquires the right image and the infrared camera 2L acquires the left image. Furthermore, because in the right image and the left image the horizontal position on the display screen for the same object appears displaced, this displacement (that is, parallax) enables calculation of the distance from the vehicle 10 to the object.

Next in step S4, the right image obtained by the infrared camera 2R is assigned as the reference image, and binarization processing of this image signal is performed, that is, regions brighter than a predetermined luminance threshold value ITH are set to “1” (white), and darker regions are set to “0” (black).

The processing of steps S4 through S9 above is executed for the reference image obtained from the binarization processing (for example, the right image).

Next, in step S5, the image data obtained as a result of binarization processing for the infrared images is converted into run length data. In the run length data, regions that have become white as a result of the binarization processing are displayed as lines at the pixel level. Each line is set to have the width of one pixel in the y direction and the length of an appropriate number of pixels in the x direction.

Next, in step S6, labeling of the object is performed for the image data converted into the run length data.

Next, in step S7, the object is extracted according to the labeling of the object. Here, in the case where lines including equal x direction coordinates are adjacent to each other among respective lines of the run length data, the adjacent lines are recognized to be forming a single object.

Next, in step S8, the center of gravity G of the extracted object, the area S, and the aspect ratio ASPECT of the circumscribed quadrangles are calculated.

Here, the areas S are calculated by adding the lengths (run (i)−1) of each run length data for the same object, assuming that the run length data of the object labeled A is (x (i), y (i), run (i), A) (i=0, 1, 2, . . . , N−1; where N is an arbitrary nonnegative integer).

Furthermore, the coordinates (xc, yc) of the center of gravity G of the object labeled A are calculated by multiplying the length (run (i)−1) of each run length data by the coordinates x (i) or y (i) (that is, (run (i)−1)×x (i) or (run (i)−1)×y (i)), adding the multiplication products for the same object, and dividing the result by the area S.

In addition, the aspect ratio ASPECT is calculated as the ratio Dy/Dx of the length Dy in the vertical direction of a quadrangle circumscribed around the object labeled A against the length Dx in the horizontal direction.

Since the run length data is represented by the number of pixels (number of coordinates) (=run (i)), it is necessary to subtract 1 from the actual length (=run (i) −1). Furthermore, the coordinate of the center of gravity G can be substituted for the center of gravity of the circumscribed quadrangle of the object.

Next, the processing of step S9 and step S10, and the processing of step S11 to step S13 are performed in parallel.

First of all, in step S9, time tracking of the object is performed, that is, the same object is recognized in each sampling period. The time tracking is performed to determine whether two objects A and B extracted at time k, which is an analog time t discrete within a sampling period, are the same as the bodies C and D, which are extracted at the discrete time (k+1). When it is determined that the objects A and B are the same as the objects C and D, the objects C and D are relabeled as objects A and B. Then, the coordinates of each object that has been recognized (for example, the center of gravity) are stored in the memory as time series position data.

Next, in step S10, the vehicle speed VCAR detected by the vehicle speed sensor 4 and the yaw rate YR detected by the yaw rate sensor 3 are obtained, and by taking the time integral of the yaw rate YR, the turning angle θr of the vehicle 10 is calculated.

Meanwhile, in parallel to the processing in step S9 and step S10, processing for calculating the distance z between the object and the vehicle 10 is performed in step S11 through step S13. Because the processing of step S11 requires more time than that of step S9 and step S10, it is performed for a longer period than steps S9 and S10 (a period approximately three times longer than the period for steps S1 through S10, for example).

First, in step S11, one of a plurality of the objects tracked in the binarized image data of the reference image (for example, the right image) is selected, and for example, the entire region enclosing the selected object by a circumscribed quadrangle is extracted as a search image R1 from the reference image (for example, the right image).

Next, in step S12, a search region for searching an image (corresponding image) R2 corresponding to the search image R1 is set in the image (for example, the left image) that corresponds to the reference image (for example, the right image), and a correlation calculation is performed to extract a corresponding image R2. Here, for example, a search region is set in the left image according to the vertex coordinates of the search image R1, a luminance difference summation value C (a, b) which shows the degree of correlation of the search image R1 in the search region is calculated, and the region where this summation value C (a, b) is lowest is extracted as the corresponding image R2. Moreover, this correlation calculation is performed for the gray scale image, not the image data obtained from the binarization process. In the case where historical position data is available for the same object, the search region can be narrowed based on the historical position data.

Next, in step S13, the positions of the centers of gravity of both the search image R1 and the corresponding image R2, and the parallax Δd at pixel level are calculated. Furthermore, the distance between the vehicle 10 and the object that is the distance z (m), (object distance) from the infrared cameras 2R and 2L to the object, is for example calculated, based on the base length of the cameras that is the horizontal distance D (m) between center positions of the each imaging device of the infrared cameras 2R and 2L, the focus distance of the camera that is the focus distance f (m) of each lens of the infrared cameras 2R and 2L, the pixel pitch p (m/pixel), and parallax Δd (pixel), as shown in the numerical expression (1).

$\begin{matrix} {z = \frac{f \times D}{\Delta\; d \times p}} & (1) \end{matrix}$

Moreover, in step S14, when the calculation of the turning angle θr in step S10 and the calculation of the distance z in step S13 are completed, the coordinates in the image (x, y) and the distance z are converted to real space coordinates (X, Y, Z).

Here, as shown in FIG. 2 for example, the real space coordinates (X, Y, Z) are set by appointing the center position of the mounting position of the infrared cameras 2R and 2L in front of the vehicle 10 as the origin O, and the coordinates in the image are set so that the horizontal direction is the x direction and the vertical direction the y direction, with the center of the image as the origin. Furthermore, the coordinates (xc, yc) are the coordinates that have been converted from the coordinates (x, y) in the reference image (for example, the right image) into the coordinates in a virtual image obtained by aligning the origin O of the real space and the center of the image data so that they coincide, based on the relative position relationship between the mounting position of the infrared camera 2R and the origin O of the real space.

$\begin{matrix} \left. \begin{matrix} {\begin{bmatrix} X \\ Y \\ Z \end{bmatrix} = \begin{bmatrix} {x\; c \times {z/F}} \\ {y\; c \times {z/F}} \\ z \end{bmatrix}} \\ {F = \frac{f}{p}} \end{matrix} \right\} & (2) \end{matrix}$

Next, in step S15, a turning angle correction is performed to compensate for the displacement of the object on the image caused by turning of the vehicle 10. This turning angle correction processing is to compensate for displacement by Δx in the x direction in the range of the image data taken by the infrared cameras 2R and 2L, when the vehicle 10, for example, turns to the left by an angle of θr within a period from time k to (k+1). As shown in the numerical expression (3) for example, the compensated coordinates (Xr, Yr, Zr) obtained as a result of compensating the real space coordinates (X, Y, Z) are set as new real space coordinates (X, Y, Z).

$\begin{matrix} {\begin{bmatrix} {Xr} \\ {Yr} \\ {Zr} \end{bmatrix} = {\begin{bmatrix} {\cos\;\theta\; r} & 0 & {{- \sin}\;\theta\; r} \\ 0 & 1 & 0 \\ {\sin\;\theta\; r} & 0 & {\cos\;\theta\; r} \end{bmatrix}\begin{bmatrix} X \\ Y \\ Z \end{bmatrix}}} & (3) \end{matrix}$

Next, in step S16, an approximated straight line LMV, which corresponds to the relative movement vector between the object and the vehicle 10, is calculated from N (N=approximately 10, for example) pieces of real space position data constituting time series data, having undergone turning angle correction, obtained for the same object during a predetermined monitoring period ΔT.

In this step S16, the most recent coordinates P (0)=(X (0), Y (0), Z (0)) and the coordinates P prior to sampling (prior to the predetermined period ΔT) (N−1)=(X (N−1), Y (N−1), Z (N−1)) are corrected to the positions on the approximated straight line LMV, and the corrected coordinates Pv (0)=(Xv (0), Yv (0), Zv (0)) and Pv (N−1)=(Xv (N−1), Yv (N−1), Zv (N−1)) are calculated.

This procedure obtains the relative movement vector as a vector moving from the coordinates Pv (N−1) towards Pv (0).

By obtaining a relative movement vector by calculating an approximated straight line which approximates the relative movement track of the object relative to the vehicle 10 from a plurality (for example, N) pieces of real space position data within the predetermined monitoring period ΔT, it is possible to estimate with better accuracy whether or not there is a possibility of collision between the vehicle 10 and an object, reducing the effect of position detection errors.

Next, in step S17, in the warning determination processing based on the possibility of collision between the detected object and the vehicle 10, it is determined whether or not the detected object is subject to warning.

When the result of this determination is “NO”, the flow returns to step S1, and the processing of step S1 to step S17 described above is repeated.

On the other hand, when the result of this determination is “YES”, the flow proceeds to step S18.

Moreover, in step S18, in the warning output determination process corresponding to whether or not the driver of the vehicle 10 is operating the brake based on the output BR of the brake sensor 5, it is determined whether or not the warning output is required.

When the determination result in step S18 is “NO”, for example, in the case where a degree of acceleration Gs (positive in the deceleration direction) is greater than a predetermined threshold GTH while the driver of the vehicle 10 is operating the brake, it is determined that the collision can be avoided by the brake operation, the flow returns to step S1, and the processing of step S1 to step S18 described above is repeated.

On the other hand, when the determination result in step S18 is “YES”, for example in the case where a degree of acceleration Gs (positive in the deceleration direction) is not greater than the predetermined threshold GTH while the driver of the vehicle 10 is operating the brake, or in the case where the driver of the vehicle 10 is not operating the brake, the possibility of collision is determined to be high and the flow proceeds to step S19.

The predetermined threshold value GTH is a value which corresponds to acceleration which would result in the vehicle 10 stopping after a traveling distance not greater than the distance Zv (0) between the object and the vehicle 10 in the case where the degree of acceleration Gs during the brake operation is maintained.

Then, in step S19, an audible sound warning is output, for example, through the speaker 6, or visual display warning is output, for example, through the display device 7, or tactual warning is output by generating a fastening force that is tactually perceivable to the driver with generation of a predetermined tension to the seatbelt, or by generating vibration (steering vibration), to a steering wheel for example, that is tactually perceivable to the driver.

Next, in step S20, for example, the image data obtained from the infrared camera 2R is output to the display device 7 to display the relatively approaching object as a highlighted image.

Hereunder, the warning determination processing in step S17 mentioned above is described, with reference to the attached drawings.

This warning determination processing determines the possibility of a collision between the vehicle 10 and a detected object based on the collision determination processing, processing to determine whether or not an object is in an approach determination region, intrusion collision determination processing, manmade structure determination processing, and pedestrian determination processing, as shown in FIG. 4. The description below makes reference to an example as shown in FIG. 5, in which an object 20 is traveling at a velocity Vp in the direction at a substantially 90° angle relative to the traveling direction of the vehicle 10 (for example the Z direction).

First of all, in step S31 shown in FIG. 4, collision determination processing is performed. This collision determination processing calculates the relative velocity Vs of the vehicle 10 and the object 20 in the Z direction in the case where, as in FIG. 5, the object 20 approaches from a distance of Zv (N−1) to a distance of Zv (0) during a time period ΔT, and assuming that the heights of both the vehicle 10 and the object 20 are not greater than a predetermined ground clearance H and the relative velocity Vs is maintained, determines whether or not the vehicle 10 and the object 20 will collide within the predetermined time allowance Ts.

When the determination result is “NO”, the flow proceeds to step S37 that is described later.

On the other hand, when the result of this determination is “YES”, the flow proceeds to step S32.

Also, the time allowance Ts is intended to allow determination of the possibility of a collision in advance of the estimated collision time by a predetermined length of time Ts, and is set to approximately 2 to 5 seconds, for example. Furthermore, the predetermined ground clearance H is set to approximately twice the height of the vehicle 10, for example.

Next, in step S32, whether or not the object is within an approach determination region is determined. As shown in FIG. 6 for example, in a region AR0 which can be monitored by the infrared cameras 2R and 2L, this determination processing determines whether or not the object is within a region AR1, which is a distance (Vs×Ts) closer to the vehicle 10 than a front position Z1, and which has a total width (α+2β) with predetermined width β (for example approximately 50 to 100 cm) added to both sides of the width α of the vehicle 10 in the vehicle lateral direction (that is the X direction), and which has the predetermined ground clearance H; that is, an approach determination region AR1 where there is a high likelihood of a collision occurring with the vehicle 10 if the object stays in that location.

When the determination result is “YES”, the flow proceeds to step S34 that is described later.

On the other hand, when the result of this determination is “NO”, the flow proceeds to step S33.

Then in step S33, intrusion collision determination processing is performed to determine whether or not there is a possibility of the object entering the approach determination region and colliding with the vehicle 10. As shown in FIG. 6 for example, this intrusion collision determination processing determines whether or not there is a possibility of the object in intrusion determination regions AR2 and AR3 at the ground clearance H, where these regions are outside the approach determination region AR1 in the vehicle lateral direction (that is, the x direction), moving and entering the approach determination region AR1 and colliding with the vehicle 10.

When the determination result is “YES”, the flow proceeds to step S36, which is described later.

On the other hand, when the determination result is “NO”, the flow proceeds to step S37, which is described later.

Then, in step S34, manmade structure determination processing is performed to determine whether the object is a manmade structure or not. This manmade structure determination processing determines that the object is a manmade structure and excludes the object from the warning determination if certain characteristics such as those mentioned below are detected, meaning that the object cannot be a pedestrian.

When the result of this determination is “NO”, the flow proceeds to step S35.

On the other hand, when the result of this determination is “YES”, the flow proceeds to step S37.

Then, in step S35, pedestrian determination processing is performed to determine whether the object is a pedestrian or not.

When the result of the determination in step S35 is “YES”, the flow proceeds to step S36.

On the other hand, when the result of the determination in step S35 is “NO”, the flow proceeds to step S37, which is described later.

Then, in step S36, when in step S33 there is a possibility of the object entering the approach determination region and colliding with the vehicle 10, (YES in step S33), or in step S35 the object determined possibly to be a pedestrian is not a manmade structure, (YES in step S35), it is determined that there is a possibility of the vehicle 10 colliding with the detected object and a warning is justified, and the processing is terminated.

In step S37, on the other hand, when in step S31 there is no possibility of a collision between the vehicle 10 and the object within the predetermined time allowance Ts, (NO in step S31), or in step S33 there is no possibility of the object entering the approach determination region and colliding with the vehicle 10, (NO in step S33), or in step S34 a determination is made that the object is a manmade structure, (YES in step S34), or when the object determined not to be a manmade structure in step S34 is not a pedestrian, (NO in step S35), it is determined that there is no possibility of a collision between the object and the vehicle 10 and a warning is not justified, and the processing is terminated.

Hereinafter, as the manmade structure determination processing in step S34 mentioned above, processing to distinguish a manmade structure having a shape similar to a pedestrian, especially a similar shape and height of a head that emits heat, is described.

As shown for example in FIG. 7, on a reference image (for example, the right image obtained from the infrared camera 2R), this manmade structure determination processing sets a target region (mask) OA that is a horizontal edge detection target, being a region that includes a binarized object OB.

For example, given the coordinates (xb, yb) at the top left point QL of the circumscribed quadrangle QB of the binarized object OB, a width Wb of the circumscribed quadrangle and a height Hb of the circumscribed quadrangle, if the width dxP of the mask OA is made a predetermined value (for example, the width Wb of the circumscribed quadrangle +2×a predetermined value M_W; the predetermined value M_W being 1 pixel), and the height dyP of the mask OA is made a predetermined value (for example, 2×the predetermined value M_H; the predetermined value M_H being a value expanding a predetermined height MASK_H of an actual space (for example, 30 cm) on an image, wherein the predetermined value M_H=focal length f×predetermined height MASK_H/object distance z), the coordinates (xP, yP) of the top left point AL of the mask OA are (xP=xb−M_W, yP=yb−M_H).

Then it is determined whether or not an edge filter output value Kido_buff obtained by applying an appropriate edge filter for detecting horizontal edges to within the mask OA on the gray scale image is greater than a predetermined threshold value KIDO_FIL_TH (for example, 10 tones).

When the result of this determination is “YES”, the setting is made that a horizontal edge exists.

Meanwhile, when the result of this determination is “NO”, the setting is made that a horizontal edge does not exist.

Then, as shown for example in FIG. 8, at each horizontal line I (I is an integer, I=0, . . . , dyP−1) set in the mask OA, the ratio of the number of pixels where the horizontal edge is determined to exist (edge detection pixel number) to the total pixel number constituting the horizontal line, that is, the inclusion ratio RATE (I) of the horizontal edge (=100×edge detection pixel number×(dxP−2×M_W)) is calculated.

Then, among the horizontal edges included in the horizontal line I where the inclusion ratio RATE (I) of the horizontal edge is greater than a prescribed threshold value RATE_TH (for example, 50%) (RATE (I)>RATE_TH), as shown for example in FIG. 9, the height position of the horizontal edge where the height position of the horizontal edge is a maximum (that is the highest position in the vertical direction) is set as the edge detection position j_FL_TOP.

Then, it is determined whether or not the difference between the top end position OBJ_TOP of the binarized object detected from the image data converted to run length data and the edge detection position j_FL_TOP (|OBJ_TOP−j_FL_TOP|) is greater than a prescribed threshold value DIFF_H_TH.

The prescribed threshold value DIFF_H_TH is, for example, a value expanding a predetermined height H_TH (for example, 10 cm) of the actual space on an image, wherein the prescribed threshold value DIFF_H_TH=focal length f×predetermined height H_TH/object distance z.

When this determination result is “NO”, that is, when the top end position OBJ_TOP of the binarized object and the edge detection position j_FL_TOP are approximately the same position, then as shown in FIG. 10 it is determined that the maximum height position of the horizontal edge where the decrease in the luminance value is relatively steep (edge detection position j_FL_TOP) is detected at approximately the same position as the top end position OBJ_TOP of the binarized object, and the binarized object is determined to be something other than a manmade structure (for example, the head of a pedestrian) extending in the vertical direction (for example, upward in the vertical direction), and the processing is terminated.

When this determination result is “YES”, that is, when the difference between the top end position OBJ_TOP of the binarized object and the edge detection position j_FL_TOP is relatively great, for example, the decrease in the luminance value in the vertical direction is relatively moderate, then it is determined that the binarized object is a manmade structure extending in the vertical direction (for example, upward in the vertical direction), and the processing is terminated.

That is, when the binarized object is determined to be a manmade structure extending in the vertical direction (for example, upward in the vertical direction) with the fluctuation in the luminance value along the vertical direction being relatively moderate, for example, a horizontal edge is detected only near the peak position where the luminance value is a maximum, the horizontal edge becomes difficult to detect at the region where the luminance value changes to a decreasing trend, and the difference between the top end position OBJ_TOP of the binarized object and the edge detection position j_FL_TOP increases.

According to the vehicle surroundings monitoring apparatus described above, a manmade structure extending in a vertical direction (for example, upward in the vertical direction) as a type of object in which a horizontal edge is difficult to detect within the prescribed threshold value DIFF_H_TH from the top end position OBJ_TOP of the binarized object and something other than a manmade structure, for example, a pedestrian with a horizontal edge easily detected at the top end position OBJ_TOP of the binarized object, can be precisely distinguished.

In the embodiment described above, when the difference between top end position OBJ_TOP of the binarized object and the edge detection position j_FL_TOP is greater than the prescribed threshold value DIFF_H_TH, the binarized object is determined to be a manmade structure extending in the vertical direction (for example, upward in the vertical direction), but it is not limited thereto. For example, when the difference between the bottom end position OBJ_BOT of the binarized object and the edge detection position j_FL_BOT is greater than the prescribed threshold value DIFF_H_TH, the binarized object may be determined to be a manmade structure extending in the vertical direction (for example, downward in the vertical direction).

That is, in the manmade structure determination processing, as shown in FIG. 11, on a reference image (for example, the right image obtained from the infrared camera 2R), there is set a target region (mask) OA that is a horizontal edge detection target, being a region that includes at least the lower portion of the binarized object OB.

For example, given the coordinates (xb, yb) at the top left point QL of the circumscribed quadrangle QB of the binarized object OB, a width Wb of the circumscribed quadrangle and a height Hb of the circumscribed quadrangle, if the width dxP of the mask OA is made a predetermined value (for example, the width Wb of the circumscribed quadrangle+2×a predetermined value M_W; the predetermined value M_W being 1 pixel), and the height dyP of the mask OA is made a predetermined value (for example, 2×the predetermined value M_H; the predetermined value M_H being a value expanding a predetermined height MASK_H of an actual space (for example, 30 cm) on an image, wherein the predetermined value M_H=focal length f×predetermined height MASK_H/object distance z), the coordinates (xP, yP) of the top left point AL of the mask OA are (xP=xb−M_W, yP=yb+Hb−M_H).

Then it is determined whether or not an edge filter output value Kido_buff obtained by applying an appropriate edge filter for detecting horizontal edges to within the mask OA on the gray scale image is greater than a predetermined threshold value KIDO_FIL_TH (for example, 10 tones).

When the result of this determination is “YES”, the setting is made that a horizontal edge exists.

Meanwhile, when the result of this determination is “NO”, the setting is made that a horizontal edge does not exist.

Then, as shown for example in FIG. 12, at each horizontal line I (I is an integer, I=0, . . . , dyP−1) set in the mask OA, the ratio of the number of pixels where the horizontal edge is determined to exist (edge detection pixel number) to the total pixel number constituting the horizontal line, that is, the inclusion ratio RATE (I) of the horizontal edge (=100×edge detection pixel number×(dxP−2×M_W)) is calculated.

Then, among the horizontal edges included in the horizontal line I where the inclusion ratio RATE (I) of the horizontal edge is greater than a prescribed threshold value RATE_TH (for example, 50%) (RATE (I)>RATE_TH), as shown for example in FIG. 9, the height position of the horizontal edge where the height position of the horizontal edge is a minimum (that is the lowest position in the vertical direction) is set as the edge detection position j_FL_BOT.

Then, it is determined whether or not the difference between the bottom end position OBJ_BOT of the binarized object detected from the image data converted to run length data and the edge detection position j_FL_BOT (|OBJ_BOT−j_FL_BOT|) is greater than a prescribed threshold value DIFF_H_TH.

The prescribed threshold value DIFF_H_TH is, for example, a value expanding a predetermined height H_TH (for example, 10 cm) of the actual space on an image, wherein the prescribed threshold value DIFF_H_TH=focal length f×predetermined height H_TH/object distance z.

When this determination result is “NO”, that is, when the bottom end position OBJ_BOT of the binarized object and the edge detection position j_FL_BOT are approximately the same position, then as shown in FIG. 13 it is determined that the minimum height position of the horizontal edge where the decrease in the luminance value is relatively steep (edge detection position j_FL_BOT) is detected at approximately the same position as the bottom end position OBJ_BOT of the binarized object, and the binarized object is determined to be something other than a manmade structure (for example, the body of a pedestrian) extending in the vertical direction (for example, downward in the vertical direction), and the processing is terminated.

When this determination result is “YES”, that is, when the difference between the bottom end position OBJ_BOT of the binarized object and the edge detection position j_FL_BOT is relatively great, for example, the decrease in the luminance value in the vertical direction is relatively moderate, then it is determined that the binarized object is a manmade structure extending in the vertical direction (for example, downward in the vertical direction), and the processing is terminated.

That is, when the binarized object is determined to be a manmade structure extending in the vertical direction (for example, downward in the vertical direction) with the fluctuation in the luminance value along the vertical direction being relatively moderate, for example, a horizontal edge is detected only near the peak position where the luminance value is a maximum, the horizontal edge becomes difficult to detect at the region where the luminance value changes to a decreasing trend, and the difference between the bottom end position OBJ_BOT of the binarized object and the edge detection position j_FL_BOT increases.

In this modification, a manmade structure extending in a vertical direction (for example, downward in the vertical direction) as a type of object in which a horizontal edge is difficult to detect within the prescribed threshold value DIFF_H_TH from the bottom end position OBJ_BOT of the binarized object and something other than a manmade structure, for example, a pedestrian with a horizontal edge easily detected at the bottom end position OBJ_BOT of the binarized object, can be precisely distinguished.

While preferred embodiments of the invention have been described and illustrated above, it should be understood that these are exemplary of the invention and are not to be considered as limiting. Additions, omissions, substitutions, and other modifications can be made without departing from the spirit or scope of the present invention. Accordingly, the invention is not to be considered as being limited by the foregoing description, and is only limited by the scope of the appended claims. 

1. A vehicle surroundings monitoring apparatus that extracts a body present in the surroundings of a vehicle as an object, based on an image captured by an infrared imaging device, comprising: a binarized object extraction device that extracts a binarized object from image data obtained by binarizing a gray scale image of the image; a horizontal edge detection device that detects a horizontal edge in the gray scale image; a horizontal edge determination device that determines whether or not the horizontal edge detected by the horizontal edge detection device exists in a prescribed range from top end or bottom end of the binarized object extracted by the binarized object extraction device; and an object type determination device that determines a type of object based on a determination result of the horizontal edge determination device.
 2. The vehicle surroundings monitoring apparatus according to claim 1, further comprising: a pedestrian recognition device that recognizes pedestrians present in the surroundings of a vehicle based on the image, the pedestrian recognition device executing pedestrian recognition processing on the object when the object is determined to be something other than a manmade structure, or to be a pedestrian, by the object type determination device.
 3. The vehicle surroundings monitoring apparatus according to claim 1, further comprising: a warning output device that outputs a warning directed to the object when the object is determined by the object type determination device to be something other than a manmade structure or to be a pedestrian. 